2019 CSDMS meeting-008
A mountain-to-coast hydrogeomorphic modeling framework for flood risk prediction
Deposition of sediment from upland sources has the potential to increase ﬂood risk in downstream riverside communities by reducing the carrying capacity of rivers and causing overbank ﬂow. However, the morphodynamic response of rivers to variable upstream sediment supply remains poorly understood, and operational flood models do not account for sediment in flood prediction.
We introduce a framework for integrating source-to-sink sediment dynamics using coupled hydrological, hydrodynamic and landscape evolution models to quantify and better predict flooding events. A Distributed Hydrology Soil Vegetation Model is used to simulate upland streamflow and land coverage over numerical grids of river networks. Modules from the Python toolkit, Landlab, generate and route sediment from mountain sources (i.e. landslides, exposed glacial till) in the same domain. Streamflow and sediment from these upland models are delivered to a Delft3D hydrodynamic, sediment transport and morphodynamic model to characterize the effects of sediment-routing on lowland, coastal floodplains and investigate the impact on flood risk. This modeling framework is tested for three Puget Sound, WA basins: the Nooksack River, Skagit River and Mt. Rainier drainage, where gage analysis performed on historic USGS indicates regional morphodynamic patterns, with potential implications on flood risk. To ensure accurate model-coupling, the model ensemble is tested in an idealized, Landlab-generated domain.
Funded by the National Science Foundation.